Abstract
This paper describes a biologically motivated local context operator to improve low-level visual feature representations. The computation borrows the idea from the primate visual system that different visual features are computed with different speeds in the visual system and thus they can positively affect each other via early recurrent modulations. The modulation improves visual representation by suppressing responses with respect to background pixels, cluttered scene parts and image noise. The proposed local contextual computation is fundamentally different from exiting approaches that involve “whole scene” perspectives. Context-modulated visual feature representations are tested in a variety of existing saliency algorithms. Using real images and videos, we quantitatively compare output saliency representations between modulated and non-modulated architectures with respect to human experimental data. Results clearly demonstrate that local contextual modulation has a positive and consistent impact on the saliency computation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, C.H., Van Essen, D.C.: Shifter circuits: a computational strategy for dynamic aspects of visual processing. PNAS 84(17), 6297–6301 (1987)
Bar, M.: Visual objects in context. Nat. Rev. Neurosci. 5(8), 617–629 (2004)
Bradley, D.C., Goyal, M.S.: Velocity computation in the primate visual system. Nature Reviews Neuroscience 9(9), 686–695 (2008)
Bruce, N.D., Tsotsos, J.K.: Spatiotemporal saliency: Towards a hierarchical representation of visual saliency. In: Paletta, L., Tsotsos, J.K. (eds.) Attention in Cognitive Systems, pp. 98–111. Springer, Heidelberg (2009)
Bruce, N.D.B., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3) (2009)
Bullier, J.: Integrated model of visual processing. Brain Research Reviews 36(2-3), 96–107 (2001)
Derrington, A.M., Lennie, P.: Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of macaque. The Journal of Physiology 357(1), 219–240 (1984)
Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4(12), 2379–2394 (1987)
Gabbiani, F., Krapp, H.G., Koch, C., Laurent, G.: Multiplicative computation in a visual neuron sensitive to looming. Nature 420(6913), 320–324+ (2002)
Henderson, J.M., Hollingworth, A.: High-level scene perception. Annual Review of Psychology 50(1), 243–271 (1999)
Hupé, J.M., James, A.C., Payne, B.R., Lomber, S.G., Girard, P., Bullier, J.: Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature 394(6695), 784–787 (1998)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Kaplan, E., Marcus, S., So, Y.T.: Effects of dark adaptation on spatial and temporal properties of receptive fields in cat lateral geniculate nucleus. The Journal of Physiology 294(1), 561–580 (1979)
McAdams, C.J., Maunsell, J.H.R.: Effects of attention on orientation-tuning functions of single neurons in macaque cortical area v4. The Journal of Neuroscience 19(1), 431–441 (1999)
Nowak, L., Munk, M., Girard, P., Bullier, J.: Visual latencies in areas v1 and v2 of the macaque monkey. Visual Neuroscience 12(02), 371–384 (1995)
Oliva, A.: Gist of the scene. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) The Encyclopedia of Neurobiology of Attention, pp. 251–256. Elsevier, CA (2005)
Oliva, A., Torralba, A.: Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)
Schyns, P.G., Oliva, A.: From blobs to boundary edges: Evidence for time and spatial scale dependent scene recognition. Psychological Science 5, 195–200 (1994)
Shi, X., Bruce, N., Tsotsos, J.: Fast, recurrent, attentional modulation improves saliency representation and scene recognition. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–8 (June 2011)
Ungerleider, L.G., Mishkin, M.: Two Cortical Visual Systems, ch. 18, pp. 549–586 (1982)
Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: Sun: A bayesian framework for saliency using natural statistics. Journal of Vision 8(7) (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shi, X., Bruce, N.D.B., Tsotsos, J.K. (2012). Biologically Motivated Local Contextual Modulation Improves Low-Level Visual Feature Representations. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-31295-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31294-6
Online ISBN: 978-3-642-31295-3
eBook Packages: Computer ScienceComputer Science (R0)